activity
20192026
most citedA Neural Network-Based On-device Learning Anomaly Detector for Edge Devices

4 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AR2026

PIMID: A Full-System Simulator with Intricacy and Diversity for Processing-in-Memory

Yuan He, Masaaki Kondo, Galen M. Shipman +3

Processing-in-Memory addresses the memory wall by co-locating computation with memory, but because real PIM hardware remains scarce, simulation is the primary way to explore the PI…

cs.AR2023

DAISM: Digital Approximate In-SRAM Multiplier-based Accelerator for DNN Training and Inference

Lorenzo Sonnino, Shaswot Shresthamali, Yuan He +1

DNNs are widely used but face significant computational costs due to matrix multiplications, especially from data movement between the memory and processing units. One promising ap…

quant-ph2021★ 2 cited

QECOOL: On-Line Quantum Error Correction with a Superconducting Decoder for Surface Code

Yosuke Ueno, Masaaki Kondo, Masamitsu Tanaka +2

Due to the low error tolerance of a qubit, detecting and correcting errors on it is essential for fault-tolerant quantum computing. Surface code (SC) associated with its decoding a…

cs.AR2020

Energy-Efficient On-Chip Networks through Profiled Hybrid Switching

Yuan He, Jinyu Jiao, Thang Cao +1

Virtual channel flow control is the de facto choice for modern networks-on-chip to allow better utilization of the link bandwidth through buffering and packet switching, which are…

cs.LG2019★ 4 cited

A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices

Mineto Tsukada, Masaaki Kondo, Hiroki Matsutani

Semi-supervised anomaly detection is an approach to identify anomalies by learning the distribution of normal data. Backpropagation neural networks (i.e., BP-NNs) based approaches…